1. Innovative Emerging Computer Technologies
Jacob Barhen and Neena Imam
Computing and Computational Sciences Directorate
Computational Advances for Distributed Sensing

2. Center for Engineering Science Advanced Research
Fundamental theoretical, experimental, and computational research
Mission: Support DOD and the Intelligence Community
Examples of current research topics:
Missile defense: C2BMC, HALO-2 project, flash hyperspectral imaging
Sensitivity and uncertainty analysis of complex simulation models
Laser array synchronization (directed energy, ultraweak signal detection, communications, terahertz sources)
Terascale computing devices: EnLight optical core processor, IBM multicore CELL BE, field-programmable gate arrays (FPGA)
Nanoscale science, hybrid Nanoelectronics for high-performance computing (HPC)
Anti-submarine warfare: source localization, sensor nets, Doppler-sensitive waveforms, LCCA beamforming, multisensor fusion
Quantum optics applied to cryptography
Computer networks, wireless reconfigurable sensor network
CESAR sponsors: DARPA, DOE/SC, MDA, NSF, ONR, NAVSEA, other government agencies

3. Center for Engineering Science Advanced Research
Fundamental theoretical, experimental, and computational research
For distributed sensing applications, some of the most promising advances in the computational area build upon the emergence of
multicore CELL processor
reconfigurable architectures: XtremeDSP FPGA and HyperX
terascale optical core digital devices: EnLight
CESAR sponsors: DARPA, DOE/SC, MDA, NSF, ONR, NAVSEA, other government agencies

4. Technology for petascale computing: The EnLight TM 64 prototype optical core processor
Optical core is prime contributor to the outstanding processing power
Full matrix-vector multiplication per single clock cycle
Fixed point architecture, 8-bit accuracy per clock cycle
EnLight 64 demonstrator
Enhanced by on-node FPGA-based processing and control
Includes leading edge conventional processor to deliver a full functionality
Power dissipation (at 8000 GOPS throughput):
EnLight: 40 W (single board), i.e., 5 mW per Giga MAC
DSP solution: 2.79 kW (62 boards, 16 DSPs per board), i.e. 352mW per Giga MAC

5. Threat source localization from distributed sensor net
Patrol aircraft monitoring GPS-capable sonobuoys
Application
Submerged threat (e.g., submarine in coastal waters)
Compute wavefront TDOAs (time differences of arrival) for each pair of sensors S2 S1 SN
Sensor data
acquisition
Foundational steps
TDOAs for each pair of sensors
Illustrative example
10 sonobuoys sensor net
7 detect a signal
21 TDOAs only (by symmetry)
Source localization methodologies M1–M3 M1
Maximum likelihood
Iterative least squares M2
Closed form solution M3
Constrained Lagrangian optimization

6. Delay (in Sampling Intervals)
Accuracy results
TDOA magnitude (in units of sampling intervals) versus sensor pairs (ordered lexicographically) for 7 active sensors
Exact (model) results
Sensor-inferred results computed using 64-bit floating-point FORTRAN on Intel Xeon
Exact (model) results
Sensor-inferred results computed using EnLight 64 hardware 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
TDOAs
Delay (in Sampling Intervals) 20 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
TDOAs 20 21
Noise and interference are taken as Gaussian processes with a varying power level 
For SNR >(, Nt ), perfect accuracy achieved
Nt = t = 0.08 s Np = 25

7. Signal processing for active sensor arrays
Keys to superior performance of active distributed sensor networks are
Proper waveform selection
Accurate signal/system modeling
Efficient real-time signal processing via MF bank implementation
Broadband Doppler-sensitive waveforms provide one potential solution for distributed target tracking
For wideband signals, the effect of target velocity is no longer approximated as a simple “shift” in frequency: Doppler effect includes compression/stretching of the transmitted pulse
Demonstrating that this can be done with minimal power consumption will
Enable additional capabilities for future remote surveillance and combat systems
Provide a building block for other processing-heavy system functions such as sonars, underwater communications, beamforming of large arrays, etc.

8. Matched filter calculation on EnLight-64 hardware
Accuracy comparison
-30
MATLAB Alpha
MATLAB Alpha
Hardware Implementation Results Time Performance
Intel Dual
Xeon
Enlight
64a
256
Specs
2 GHz
1 GB RAM
60 MHz
16.67 ns
125 MHz
8ns
FFT 2 32
128
Timing
9,6262 ms
1.41 ms
0.17 ms
-35
-40
Output of filter #1, dB
-45
-50
-55
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
Range (meters)
Computation parameters
FFT: 80K complex samples  number of filter banks
33 filter banks: 32 Doppler cells, 1 target echo
Speed-up factor per processor
E 64: 6,826  2 > 13,000 actual hardware
E 256: 56,624  2 > 113,000 simulator

9. Computational Challenge Missile defense requires hyperspectral imagery for target kill assessment and spectral analysis of high-impact scenarios
Orbital signatures
Exo-atmospheric
target characterization
Counter-
measure signatures
Vehicle
separation
Plume signatures
Target
signatures
Chemical
releases
Kill assessment or miss distance
Trajectory reconstruction
Booster tracks
This chart provides a brief schematic view of the major missions that HALO II was designed to address:
MDA has a full range of test and evaluation needs that require an extremely capable and well characterized sensor system. The span of data types and evaluation needs is cited with thumb-nail examples of impact, reentry, boost and trajectory estimation. The capabilities and flexibility of the HALO II will permit a large number of issues to be effectively examined using high quality EO/IR data.
Boost phase interests include resolved plume and hardbody phenomenology, tracking studies and the ability to assist failure resolution. Boost phase transitions to the Midcourse after the cutoff of main propulsion and the deployment or ejection of items, such as reentry vehicles (RVs). The Mid-course interests include the the signatures and tracking aspects of various objects including debris, fuel, hardbodies and decoys. The Reentry or terminal phase begins when object trajectories and dynamics are perturbed by the tenuous atmosphere. The final aspect of BMD testing to be supported by the HALO II will be for intercept characterization and photo documentation of the entire event sequence.
Non-BMD goals can span the range of Satellite tracking / Signatures to characterization of ground targets.
Interceptor
performance
Failure
diagnostics
Photo documentation
FOR
Flash radiometry
Airborne asset
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10. Flash hyperspectral imaging
Model
Object cube expressed as vector f with N elements: N = Nx  Ny  Nλ
Finite set of measurements, denoted by FPA data vector { gm | m = 1, ..M }, where M is the number of detector elements
Imaging system described by means of M  N sparse matrix H, determined experimentally
Image Reconstruction
To date: mixed expectation ML optimization
New: CESAR noise-corrected sparse CG
Computation
Nx = Ny ≥ 256, Nλ ≥ 64  N ≥
M ≈ 8192   M ≥
Need: reconstruction time window ≤5 ms
Past performance: over 40 m on Intel Xeon (on much smaller object)
CESAR speed-up targets:
Factor 1,000-10,000 via algorithms
Factor 200 via CELL hw (single node)
Objective is to collect a set of registered, spectrally contiguous images of a scene’s spatial radiation distribution within the shortest possible data collection time.
FPA
Objective
Field stop
Disperser
Reimaging
lens
Collimator
CTIS uses dispersive optics to eliminate scanning

11. Hyperspectral object reconstruction
CTIS Toeplitz block structure 256  128 density = 11.3%
600
500
Mixed expectation
Attractor dynamics
Sparse conjugate gradient
400
300
Reconstruction Error (norm)
200
100 10 20 30 40 50 60 70 80 90
100
Iterations

12. Contacts Jacob Barhen Neena Imam
Center for Engineering Science Advanced Research
Computer Science and Mathematics
(865)
SIPRNET:
Neena Imam
Center for Engineering Science and Advanced Research
(865)
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